Conditioning provision system, conditioning provision method, conditioning provision program, and conditioning provision server

Abstract

This system provides a conditioning delivery system, a conditioning delivery method, a conditioning delivery program, and a conditioning delivery server that can provide conditioning that promotes future behavioral change according to the generation, background, and purpose of diverse target groups. [Solution] A conditioning provision system 100 that visualizes future predictions based on the behavior of a subject and supports conditioning for each subject, comprising: a data acquisition unit that acquires conditioning data; a process scenario generation unit that generates a plurality of future conditioning process scenarios; a digital twin calculation unit that generates a current subject profile and a future subject profile and simulates time-series changes over a predetermined period in a digital twin environment; and a display control unit that visualizes the subject's conditioning information.

Description

Technical Field

[0001] The present invention relates to a conditioning provision system, a conditioning provision method, a conditioning provision program, and a conditioning provision server.

Background Art

[0002] Conventionally, as a system for presenting the health status of a subject using a character image, for example, a health status management system of Patent Document 1 and an information processing system for displaying an avatar corresponding to the health status of a subject in a digital twin environment have been proposed.

[0003] The health status management system disclosed in Patent Document 1 makes the changes in the display character more accurate and continuous from a health medical perspective, making it more realistic. Also, as a health management barometer, by characterizing a photo image of oneself and using it, it enables a more realistic health management in a game-like feeling.

[0004] In addition, the information processing system disclosed in Patent Document 2 is provided by an information processing system that supports a digital twin environment, continuously acquires first information regarding the living body of a first user, and determines the state of an avatar associated with the first user so as to correspond to the health status of the first user estimated based on the first information. The avatar is used as a character that acts in an event in a virtual space, and the state of the avatar is configured to be sequentially changeable according to the first information that is visible to the first user and continuously acquired during the event.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

[0006] Here, individual conditioning (advice, guidance, etc.) for maintaining and improving one's own health is different and individual to each person, and changes depending on the time and timing, so opportunities to receive conditioning are limited. For this reason, there is a concern that it is difficult for individuals to maintain their health status and engage in purposeful health promotion. In this regard, the health status management system disclosed in Patent Document 1 reflects and displays the day's judgment result on the person's character image based on the previous day's judgment result, making it difficult to support conditioning that is tailored to the generation, background, and purpose of diverse subjects. Furthermore, in the information processing system disclosed in Patent Document 2, the state of the avatar provided in the digital twin environment corresponds to the subject's health status, but is limited to the time of the event, making it difficult to support conditioning that promotes future behavioral change.

[0007] Therefore, the present invention was devised in view of the above-mentioned problems, and its objective is to provide a conditioning provision system, a conditioning provision method, a conditioning provision program, and a conditioning provision server that can provide conditioning that promotes future behavioral change according to the generation, background, and purpose of diverse target individuals. [Means for solving the problem]

[0008] The conditioning provision system according to the first invention is a conditioning provision system that visualizes future predictions based on the behavior of the subject and supports conditioning for each subject, comprising a data acquisition unit that acquires the subject's conditioning data, and the conditioning data acquired by the data acquisition unit This is input into a pre-trained time series prediction model, and based on the prediction results of multiple different behavioral change patterns output by the time series prediction model, A process scenario generation unit generates multiple future conditioning process scenarios corresponding to different behavioral changes, and based on the conditioning data, Currently, an avatar representing the target person is generated,In the aforementioned future conditioning process scenario Avatars representing future individuals that reflect changes in body shape, activity level, or health risks corresponding to each behavioral change pattern. Generate and in a digital twin environment From the avatar of the current target person to the avatar of the future target person The system is characterized by comprising a digital twin calculation unit that simulates time-series changes over a predetermined period, and a display control unit that visualizes the results of the simulation as conditioning information for the subject.

[0009] The conditioning provision system according to the second invention is characterized in that, in the first invention, the conditioning data acquired by the data acquisition unit includes at least one of the following: biological data relating to the subject, exercise data, sleep data, dietary data, stress data, activity data relating to the subject's activities, or behavioral data relating to past behavioral changes provided.

[0010] The conditioning provision system according to the third invention, in the first invention, the process scenario generation unit uses data including at least one of the following: past conditioning data of the subject, anonymized case data collected from multiple users, social data including economic indices, or climate-related environmental data to train a time series prediction model. The conditioning data acquired by the data acquisition unit is input, and based on the prediction data of multiple different behavioral changes output from the time series prediction model, This method is characterized by generating future conditioning process scenarios that predict multiple different behavioral changes.

[0011] The conditioning provision system according to the fourth invention is characterized in that, in the first invention, the digital twin calculation unit generates a future subject profile in each future conditioning process scenario as an avatar that includes at least body type, activity level, or health risk, and simulates the time-series changes of the avatar.

[0012] The conditioning provision system according to the fifth invention is characterized in that, in the fourth invention, the display control unit visualizes behavioral changes in the future subject by displaying in parallel the current subject image generated by the digital twin calculation unit and the future subject image of the digital twin corresponding to the future conditioning process scenario, or by switching between multiple avatars.

[0013] The conditioning provision system according to the sixth invention is characterized in that, in the first invention, the display control unit identifies a branching point in the state transition in each future conditioning process scenario and presents options for behavioral changes related to the branching point.

[0014] The conditioning provision system according to the seventh invention is characterized in that, in the first invention, the data acquisition unit further acquires behavioral data relating to the behavioral changes of the subject that were carried out based on a future conditioning process scenario.

[0015] The conditioning provision system according to the eighth invention includes, in the seventh invention, a difference analysis unit that analyzes the difference between the behavioral data acquired by the data acquisition unit and the future behavioral changes in the generated future conditioning process scenario, and based on the difference Refer to differential information, recommended conditions, or action period information. It further includes an action plan output unit that outputs an action plan recommended for the target person. The action plan output unit generates and outputs an action plan that includes at least one of the following, based on the difference information, the recommended conditions, or the action period information: action goal, action period, recommended action, activity intensity, diet / nutrition, sleep / rest. It is characterized by the following:

[0016] The conditioning provision system according to the ninth invention is: Invention #8 In this case, the display control unit is The data acquired by the aforementioned data acquisition unit Conditioning data, The process scenario generated by the aforementioned process scenario generation unit Future conditioning process scenarios, The behavioral changes corresponding to the aforementioned future conditioning process scenario, and the subject profile generated by the digital twin calculation unit. Avatar, or Output by the aforementioned action plan output unit Information regarding the action plan In addition to the visualization of the simulation results described in claim 1, image information including graphs or figures for explaining these contents is used. It is characterized by generating and outputting the image information.

[0017] The conditioning provision method according to the 10th invention is: Executed on a computerA conditioning provision method for visualizing future predictions based on the actions of a target person and assisting conditioning for each target person, comprising a data acquisition step of acquiring conditioning data of the target person, and the conditioning data acquired by the data acquisition step This is input into a pre-trained time series prediction model, and based on the prediction results of multiple different behavioral change patterns output by the time series prediction model, A process scenario generation step of generating a plurality of future conditioning process scenarios corresponding to different behavior variations, and based on the conditioning data Currently, an avatar representing the target person is generated, A future target person image modeling the behavior variation in the future conditioning process scenario and An avatar is generated that represents the future profile of the target individual, reflecting changes in body shape, activity level, or health risks corresponding to each behavioral change pattern. In a digital twin environment From the avatar of the current target person to the avatar of the future target person A digital twin calculation step of simulating time-series changes over a predetermined period, and a display control step of visualizing the result of the simulation as conditioning information of the target person, characterized by comprising these steps

[0018] The conditioning provision program according to the 11th invention causes a computer to execute the conditioning provision method according to the 10th invention, characterized by this

[0019] The conditioning provision server according to the 12th invention is a conditioning provision server provided to a plurality of client terminals via a network, comprising a communication unit that receives conditioning data of a target person transmitted from a client terminal, and the conditioning data acquired by the communication unit This is input into a pre-trained time series prediction model, and based on the prediction results of multiple different behavioral change patterns output by the time series prediction model, A process scenario generation unit that generates a plurality of future conditioning process scenarios corresponding to different behavior variations, and based on the conditioning data Currently, an avatar representing the target person is generated, In the future conditioning process scenario Avatars representing future individuals that reflect changes in body shape, activity level, or health risks corresponding to each behavioral change pattern. Generate, and in a digital twin environment From the avatar of the current target person to the avatar of the future target person A digital twin calculation unit that simulates time-series changes over a predetermined period, and a transmission unit that transmits the result of the simulation to the client terminal, characterized by comprising these components

Advantages of the Invention

[0020] According to the first invention, the data acquisition unit acquires the subject's conditioning data. Therefore, the process scenario generation unit can generate multiple future conditioning process scenarios corresponding to different behavioral changes based on the conditioning data. This makes it possible to provide conditioning that promotes future behavioral changes according to the diverse ages, backgrounds, and objectives of each subject.

[0021] Furthermore, according to the first invention, the digital twin calculation unit generates a current subject profile and a future subject profile that models behavioral changes in future conditioning process scenarios, based on conditioning data. It also simulates time-series changes over a predetermined period in the digital twin environment. Therefore, the display control unit can visualize the simulation results as subject conditioning information. This makes it possible to provide conditioning that promotes future behavioral changes according to the diverse generations, backgrounds, and objectives of each subject.

[0022] In particular, according to the second invention, the conditioning data acquired by the data acquisition unit includes at least one of the following: biometric data, exercise data, sleep data, dietary data, stress data, activity data related to the subject's activities, or behavioral data related to past behavioral changes provided. Therefore, the process scenario generation unit can generate multiple future conditioning process scenarios corresponding to different behavioral changes based on the conditioning data. This makes it possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0023] In particular, according to the third invention, the process scenario generation unit generates future conditioning process scenarios that predict multiple different behavioral changes using a time-series prediction model trained with data including at least one of the following: past conditioning data of the subject, anonymized case data collected from multiple users, social data including economic indices, or environmental data related to climate. As a result, the digital twin calculation unit can generate a current subject profile and a future subject profile that models the behavioral changes in the future conditioning process scenario, and can simulate time-series changes over a predetermined period in a digital twin environment. This makes it possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0024] In particular, according to the fourth invention, the digital twin calculation unit generates a future subject profile for each future conditioning process scenario as an avatar that includes at least body type, activity level, or health risk, and simulates the time-series changes of the avatar. Therefore, the display control unit can visualize the simulation results as the time-series changes of the avatar. This makes it possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0025] In particular, according to the fifth invention, the display control unit displays in parallel the current subject image generated by the digital twin calculation unit and the future subject image of the digital twin corresponding to the future conditioning process scenario, or switches between multiple avatars. This makes it possible to visualize the behavioral changes in the future subject image. As a result, it becomes possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0026] In particular, according to the sixth invention, the display control unit identifies branching points in state transitions within each future conditioning process scenario. Therefore, it can present options for behavioral change related to these branching points. This makes it possible to provide conditioning that promotes future behavioral change according to the diverse ages, backgrounds, and objectives of each individual.

[0027] In particular, according to the seventh invention, the data acquisition unit further acquires behavioral data regarding the behavioral changes of the subject carried out based on the future conditioning process scenario. Therefore, based on the behavioral data regarding the behavioral changes of the subject, it is possible to generate multiple future conditioning process scenarios corresponding to different behavioral changes. This makes it possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0028] Furthermore, according to the eighth invention, the difference analysis unit analyzes the difference between the behavioral data acquired by the data acquisition unit and the future behavioral changes in the generated future conditioning process scenario. In addition, the behavioral plan output unit outputs a behavioral plan recommended to the subject based on the difference. As a result, the display control unit can visualize the behavioral plan recommended to the subject. This makes it possible to provide conditioning that supports behavioral plans for various subjects according to their generation, background, and purpose.

[0029] Furthermore, according to the ninth invention, the display control unit generates image information that includes at least one of the following: conditioning data, scenario data relating to future conditioning process scenarios, behavioral change data relating to behavioral change, avatar data relating to avatars, or behavioral plan data relating to behavioral plans. Therefore, this image information can be output. This makes it possible to provide conditioning that promotes future behavioral change according to the generation, background, and purpose of diverse target individuals.

[0030] Furthermore, according to the tenth invention, the data acquisition step acquires the subject's conditioning data. Therefore, the process scenario generation step can generate multiple future conditioning process scenarios that correspond to different behavioral changes based on the conditioning data. This makes it possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0031] According to the 11th invention, the conditioning provision program is executed on a computer. Therefore, the generated future conditioning process scenario, the current subject profile, and the future subject profile modeling the behavioral changes in the future conditioning process scenario can be used to simulate time-series changes over a predetermined period in a digital twin environment. This makes it possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0032] According to the 12th invention, the communication unit receives conditioning data of the subject transmitted from the client terminal. Therefore, the process scenario generation unit can generate multiple future conditioning process scenarios corresponding to different behavioral changes based on the conditioning data. This makes it possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0033] Furthermore, according to the twelfth invention, the digital twin calculation unit generates a current subject profile and a future subject profile that models behavioral changes in future conditioning process scenarios based on conditioning data, and simulates time-series changes over a predetermined period in the digital twin environment. Therefore, the transmission unit can transmit the simulation results to the client terminal. This makes it possible to provide conditioning that promotes future behavioral changes according to the generation, background, and purpose of each diverse subject. [Brief explanation of the drawing]

[0034] [Figure 1] Figure 1 is a schematic diagram showing an example of a conditioning provision system in this embodiment. [Figure 2] Figure 2 is a schematic diagram showing an example of the provision of conditioning (initial) by the conditioning provision system in this embodiment. [Figure 3] Figure 3 is a schematic diagram showing an example of the provision of conditioning (from the nth time onward) by the conditioning provision system in this embodiment. [Figure 4] Figure 4(a) is a schematic diagram showing an example of the configuration of the conditioning device in this embodiment, and Figure 4(b) is a schematic diagram showing an example of the functions of the conditioning device in this embodiment. [Figure 5] Figure 5(a) is a schematic diagram showing an example of a subject data / information table in this embodiment, and Figure 5(b) is a schematic diagram showing an example of a conditioning data / information table in this embodiment. [Figure 6] Figure 6(a) is a schematic diagram showing an example of a future conditioning process scenario information table in this embodiment, and Figure 6(b) is a schematic diagram showing an example of an action plan information table in this embodiment. [Figure 7] Figure 7 is a schematic diagram showing an example of a time series forecasting model database in this embodiment. [Figure 8] Figure 8 is a schematic diagram showing an example of a time series forecasting model database in this embodiment. [Figure 9] Figure 9 is a schematic diagram showing an example of the simulation results in this embodiment. [Figure 10] Figure 10 is a schematic diagram showing an example of an action plan provided by the conditioning provision system in this embodiment. [Figure 11] Figure 11 is a flowchart showing an example of the operation of the conditioning provision system in this embodiment. [Modes for carrying out the invention]

[0035] Hereinafter, examples of a conditioning provision system, a conditioning provision device, and a conditioning provision method in embodiments to which the present invention is applied will be described with reference to the drawings.

[0036] First, with reference to Figure 1, an example of the conditioning provision system 100 and the conditioning provision device 1 in this embodiment will be described.

[0037] The conditioning provision system 100 in this embodiment includes, for example, a conditioning provision device 1, as shown in Figure 1. The conditioning provision device 1 connects to various subject terminals 2 (smartphone 2a, smartwatch 2b), terminal 5, and server 6, which are, for example, owned by the subject, via a communication network 4. Furthermore, the conditioning provision device 1 may also connect to a camera 3 installed in, for example, a room, facility, etc. (not shown). The subject terminal 2 may be owned by, for example, an instructor (not shown) who provides guidance to the subject, or it may be a dedicated measuring device (not shown) capable of acquiring the subject's conditioning data.

[0038] The communication network 4 includes, but is not limited to, local area networks (LANs), wide area networks (WANs), the Internet, public telephone networks, mobile phone networks, satellite communication networks, optical communication networks, wireless LANs (Wi-Fi), Bluetooth®, near-field communication (NFC), 5G (fifth-generation mobile communication), mobile communication networks to be developed in the future, and other wireless communication means, and may be a combination of one or more networks.

[0039] The conditioning provision system 100, the conditioning provision device 1, and the terminal 5 are operated by the operator of the conditioning provision system 100, the service manager, or support staff, and various data and information stored on the server 6 are referenced.

[0040] Server 6 may be composed of multiple databases, for example. Server 6 may consist of multiple databases, for example, Server 6a and Server 6b. Server 6a stores various data and information related to conditioning acquired by the conditioning provision device 1, for example. Conditioning data may include, for example, biological data, exercise data, sleep data, diet data, stress data, activity data related to the subject's activities, or behavioral data related to past behavioral changes provided.

[0041] Server 6b stores, for example, the subject's past conditioning data, anonymized case data collected from multiple users, social data including economic indices, or climate-related environmental data, as well as time series forecasting models trained using such data. Server 6b may be included in, for example, server 6a, in which case it stores various data and information as server 6.

[0042] The various data and information stored on server 6 (6a, 6b) may be updated, added, or deleted as appropriate by, for example, the administrator of the conditioning provision system 100, and may also be configured to be accessible and retrieved by third parties authorized by the administrator.

[0043] (Conditioning provision system 100) Referring to Figure 2, an example of the provision of conditioning (initial) in the conditioning provision system 100 will be explained.

[0044] As shown in Figure 2, the conditioning provision system 100 acquires the subject's initial conditioning data, for example, using the conditioning provision device 1. The conditioning provision device 1 acquires, for example, the subject's basic data, such as ID, attribute data (age, gender, etc.), biometric data which is information indicating the subject's biological characteristics, exercise data related to the exercises the subject performs, health check data related to the subject's health status and symptoms, activity data related to the subject's lifestyle, exercise, participation in events, etc., and behavioral data showing the results of those activities. The conditioning provision device 1 does not acquire the subject's personal information, for example, but provides future conditioning to the subject based on the subject's attribute data, etc.

[0045] Furthermore, the conditioning device 1 acquires anonymized case data, such as anonymized case data, operational data showing the operation and mechanisms of society, and social data such as trends. In addition, the conditioning device 1 acquires environmental data, such as measured and predicted values ​​of temperature, humidity, etc., related to the subject's residence, district, and region, as well as various alert data such as weather reports and warnings from government agencies or specialized organizations, and other information.

[0046] The conditioning provision system 100 generates a future conditioning process scenario for a subject based, for example, on the subject's conditioning data acquired initially. The conditioning provision system 100 simulates the time-series changes of the current subject profile and the future subject profile in a digital twin environment, and displays the simulation results as the subject's initial conditioning information on the display unit 109 of the conditioning provision device 1.

[0047] In addition to displaying the initial simulation results as the subject's conditioning information, the conditioning provision system 100 may also display the subject's conditioning data acquired during the initial setup, for example, on the display unit 109 of the conditioning provision device 1. The conditioning data includes, for example, the subject's ID, nickname, images prepared in advance by the subject, or images provided by the conditioning provision device 1, etc. (avatar, illustration, etc.), age, gender, symptoms (medical history), BMI, etc., and does not include personal information that could identify the subject. This ensures consideration for the subject's privacy.

[0048] The conditioning data or conditioning information provided by the conditioning provision system 100 regarding the subject may be generated as images such as multiple graphs, multidimensional analysis graphs, various shapes, or codes, and displayed together with the conditioning data or conditioning information. The various images displayed may be visualized by combining multiple images, for example. The display unit 109 may also display buttons for generating future conditioning process scenarios, and may accept operations from the subject or instructor and execute processes pre-assigned to each button.

[0049] The conditioning provision system 100 generates multiple future conditioning process scenarios that correspond to different behavioral changes in the subject, for example, based on the acquired conditioning data. It then models the current subject profile, which shows the subject's current conditioning, and the behavioral changes in the future conditioning process scenarios, generating a future subject profile that simulates the current subject.

[0050] The conditioning provision system 100 displays the results of a simulation in a digital twin environment, for example, as the future conditioning of the subject on the display unit 109. The future conditioning may be displayed as a future subject profile using an avatar or the like based on multiple future conditioning process scenarios, or it may be generated by imagining the current appearance of the subject, for example, or a character preferred by the subject (not shown) may be selected, and the future subject profile may be displayed using the selected character.

[0051] The conditioning provision system 100 displays, for example, the current target profile in month ● 2025 and the future target profile based on a future conditioning process scenario in month ● 20XX in a digital twin environment. The conditioning provision system 100 displays, for example, the "current target profile" and "conditioning data" for the initial conditioning in the left half of the screen area of ​​the display unit 109.

[0052] Furthermore, the conditioning provision system 100 may display conditioning information such as a "future target person profile" indicating future conditioning, multiple "future conditioning process scenarios," and "images (e.g., multidimensionally analyzed behavior change graphs)" related to the conditioning information in the right half of the screen.

[0053] The conditioning provision system 100 may, for example, control the display to switch between and display multiple generated future conditioning process scenarios when displaying future conditioning process scenarios. Furthermore, the conditioning provision system 100 may, for example, set the future conditioning process scenario to "Scenario A" and display the future target person profile in chronological order. The conditioning provision system 100 may, for example, display a scroll bar at the bottom of the screen that shows the changes in the time series ("Past (2022)" to "Future (20XX)") and display the current target person profile generated in the past and the future target person profile that has changed in chronological order from the avatar of the future target person.

[0054] The conditioning provision system 100, via the operation of a scroll bar by the subject or instructor, for example, if the time series position is "Past (2022)", refers to database 6 to obtain the subject's conditioning data acquired in 2022, or generated conditioning information, subject profile, etc., and displays it on the display unit 109. Furthermore, if the time series position is "Future (20XX)", the conditioning provision system 100 refers to database 6 to obtain conditioning information based on generated future conditioning process scenarios, future subject profile, etc., and displays it on the display unit 109.

[0055] The conditioning provision system 100, for example, if the future conditioning process is fixed to "Scenario A" and the timeline is changed using the scroll bar, will display the conditioning information and the behavioral changes of the future subject at the changed time. This allows the subject or instructor to repeatedly refer to the behavioral changes, process, effectiveness of the guidance, and predictions in "Scenario A" in a timeline.

[0056] Furthermore, the conditioning provision system 100, for example, if the time period is fixed to "future (20XX)" by using a scroll bar, and the future conditioning process is changed from "Scenario A" to "Scenario C" in that state, will display the conditioning information and the behavioral changes of the future subject in the changed "Scenario C". This allows the subject or instructor to repeatedly refer to behavioral changes, processes, the effects of instruction, and predictions in "future (20XX)" across multiple processes.

[0057] Next, referring to Figure 3, an example of the provision of conditioning (from the nth time onward) in the conditioning provision system 100 will be explained. As shown in Figure 3, the conditioning provision system 100 acquires the subject's conditioning data from the nth time onward, for example, using the conditioning provision device 1. The conditioning provision device 1 acquires the subject's basic data as it did the first time (previous time), as well as the latest (current) biometric data, exercise data, health check data, subject activity data, and behavioral data. Based on the newly acquired subject's conditioning data, the conditioning information provided the first time, the future conditioning process scenario, the future subject profile, etc., the conditioning provision device 1 provides the generated conditioning information from the nth time onward, the future conditioning process scenario, the future subject profile, etc.

[0058] The conditioning provision device 1 may, for example, evaluate the differences in numerical values ​​of each item of the subject's conditioning data between the previous and newly acquired data. If it determines that the evaluation results indicate improvement or enhancement, it may predict the subject's future conditioning, update the previously provided conditioning information (e.g., symptoms: obesity, scenario: scenario A, future subject profile: walking) as shown in Figure 2, and provide the conditioning information provided this time (e.g., symptoms: lack of exercise, scenario: scenario A, future subject profile: running), as well as the future conditioning, future subject profile, etc.

[0059] The conditioning device 1 may also, for example, if the evaluation results worsen or decline, predict the future conditioning of the subject, update the previously provided conditioning information (e.g., symptoms: obesity, scenario: scenario A, future subject profile: walking), and provide conditioning information, future conditioning, future subject profile, etc., such as the currently provided conditioning information (e.g., symptoms: weight gain, scenario: scenario C, future subject profile: dietary restrictions / requires guidance).

[0060] Furthermore, the conditioning provision device 1 may refer to the database 6 and, for example, if a future subject profile previously generated or provided is stored as "Scenario A" of the future conditioning process scenarios, set the scroll bar at the bottom of the display unit 109 screen to "Past (2022)" to display the previous "future subject profile" on the left half of the display unit 109 screen. This makes it possible to compare and refer to past "future subject profiles" in "Scenario A" with future "future subject profiles," visualize the differences in behavioral changes of subjects over time, and encourage future behavioral changes according to the generation, background, and purpose of each diverse subject.

[0061] Furthermore, if the conditioning provider 1 is to provide conditioning to the same person in the future (for example, "Nickname: Mr. / Ms. XX"), and the same process scenario (for example, "Process Scenario A") is used, it may refer to the database 6 to retrieve the already generated future conditioning process scenario, conditioning information, future person profile, etc., and redisplay them in chronological order set by the person or instructor.

[0062] As the conditioning provision system 100, electronic devices such as personal computers (PCs) may be used, as well as electronic devices such as smartphones, tablet terminals, wearable devices, IoT (Internet of Things) devices, single-board computers such as Raspberry Pi (registered trademark), and cloud systems.

[0063] (Conditioning device 1) Next, an example of the conditioning device 1 in this embodiment will be described with reference to Figure 4. Figure 4(a) is a schematic diagram showing an example of the configuration of the conditioning device in this embodiment, and Figure 4(b) is a schematic diagram showing an example of the functions of the conditioning device in this embodiment.

[0064] The conditioning device 1, as shown in Figure 4(a) for example, comprises a housing 10, a CPU (Central Processing Unit) 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, a storage unit 104, and I / Fs 105 to 107. Each component 101 to 107 is connected by an internal bus 110.

[0065] The CPU 101 controls the entire conditioning device 1. The ROM 102 stores the operating code for the CPU 101. The RAM 103 is a work area used when the CPU 101 is operating. The storage unit 104 stores multiple information tables, various environmental information, and other various information, which will be described later. As the storage unit 104, a data storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive) can be used. For example, the conditioning device 1 may also have a GPU (Graphics Processing Unit), which is not shown. Having a GPU enables faster computation processing than usual.

[0066] I / F105 is an interface for sending and receiving various types of information with the target terminal 2, camera 3 (various sensors), other terminals 5, and server 6. It may also be an interface for sending and receiving various types of information with the target terminal 2, camera 3, other terminals 5, and server 6, etc., which are connected via the communication network 4, the Internet, or a LAN.

[0067] I / F106 is an interface for sending and receiving information with the input unit 108. The input unit 108 can be, for example, a keyboard, various measurement sensors, or a remote control. Users, administrators, or instructors using the conditioning device 1 can input commands via the input unit 108 to acquire various conditioning data, excluding the user's personal information. They can also input control commands for the user terminal 2, camera 3, or remote control to acquire conditioning data. The timing of the input commands by the user, administrator, or instructor is arbitrary.

[0068] I / F107 is an interface for sending and receiving various types of information with the display unit 109. The display unit 109 outputs, for example, notifications to the subject or instructor stored in the storage unit 104, various information related to the provision of conditioning, and the processing status of the conditioning provision device 1. As the display unit 109, for example, various displays or monitors can be used, and any type of system is acceptable, such as touch panel or voice.

[0069] Figure 4(b) is a schematic diagram showing an example of the functions of the conditioning provision device 1. The conditioning provision device 1 comprises a data acquisition unit 11, a process scenario generation unit 12, a digital twin calculation unit 13, a display control unit 14, a difference analysis unit 15, and an action plan output unit 16. The display control unit 14 displays conditioning information on the subject terminal 2 and the terminal 5 used by the instructor, and proposes action plans, etc. Furthermore, the conditioning provision device 1 may also include, for example, an update unit 17.

[0070] The functions shown in Figure 4(b) are realized by the CPU 101 executing programs stored in the storage unit 104, etc., using the RAM 103 as a work area, and may be controlled by artificial intelligence using, for example, a machine learning algorithm.

[0071] Here, with reference to Figures 5 and 6, an example of an information table showing the correspondence of the conditioning provision system 100 in this embodiment will be described.

[0072] First, Figure 5(a) is a schematic diagram showing an example of the "Subject Data / Information Table" in this embodiment. The "Subject Data / Information Table" stores various conditioning data and conditioning information about the subject acquired by, for example, the data acquisition unit 11. The "Subject Data / Information Table" stores, for example, "basic data (ID, age, gender, etc.)" that indicates information identifying the subject, "attribute data" that indicates the subject's attributes and health status, "provision process scenario information" that relates to multiple future conditioning process scenarios provided to or selected by the subject, "avatar information" which is an avatar image relating to the subject's current and future appearance, and "update date and time" that indicates the update date and time of each subject information. The "avatar information" is, for example, information for generating the subject's avatar image, such as appearance information, facial expression information, characteristic facial expressions, etc. The conditioning provision system 100 may, for example, refer to the subject's avatar information based on the subject's conditioning data and future conditioning process scenarios, and generate a future image of the subject.

[0073] Figure 5(b) is a schematic diagram showing an example of the "conditioning data / information table" in this embodiment. The "conditioning data / information table" stores various data and information related to the conditioning of each subject, for example, acquired by the data acquisition unit 11. The "conditioning data / information table" stores, for example, an "ID" to identify the subject, "related data 1" showing various data related to the subject's biological condition, exercise, etc., "related data 2" showing various data related to the subject's activities, results of activities, etc., and an "update date and time" indicating the update date and time of the conditioning data and conditioning information.

[0074] The "conditioning data / information table" may sequentially store, for example, "biometric data," "exercise data," "activity data," and "behavioral data" related to the subject, transmitted from the subject's terminal 2 (2a, 2b). This makes it possible to provide conditioning information on the subject's conditioning in real time or over a certain period, and to provide conditioning that promotes future behavioral change according to the diverse generations, backgrounds, and objectives of each subject.

[0075] Next, Figure 6(a) is a schematic diagram showing an example of the "Future Conditioning Process Scenario Information Table" in this embodiment. The "Future Conditioning Process Scenario Information Table" stores multiple future conditioning process scenarios that are referenced, for example, by the process scenario generation unit 12. The "Future Conditioning Process Scenario Information Table" stores, for example, a "Process Scenario ID" indicating the type of information referenced by the process scenario generation unit 12, "Process Scenario Configuration Information" indicating the configuration of multiple scenarios, "Avatar Generation Information" indicating the patterns related to the avatars generated in each scenario, "Attribute Data" indicating various basic data about the subject, information including symptoms, etc., and "Reference Information" indicating the type of information.

[0076] The "Future Conditioning Process Scenario Information Table" is referenced, for example, by the Digital Twin Calculation Unit 13, and based on the "Process Scenario Configuration Information" and "Avatar Generation Information" corresponding to the future conditioning process scenarios generated by the Process Scenario Generation Unit 12, a current subject profile and a future subject profile modeling the behavioral changes in the future conditioning process scenario are generated.

[0077] Figure 6(b) is a schematic diagram showing an example of the "Action Plan Information Table" in this embodiment. The "Action Plan Information Table" stores "Difference Information" which indicates the range of differences, such as the difference in updating the subject's conditioning data acquired by the data acquisition unit 11, the difference before and after the subject's behavioral change, or the difference between the future conditioning process scenario selected by the subject and the actual behavioral change; "Recommended Conditions" which indicate various conditions for the subject's recommended action plan; and "Action Period Information" which indicates the characteristics of the duration of the action plan provided to the subject. The "Action Period Information" may be stored as, for example, "Short" for a short period of a few days, "Medium" for a period of several months, or "Long" for a long period of several years.

[0078] The various correspondence tables mentioned above are updated by the update unit 17 as needed, based on the contents recorded in each table, such as the generated future conditioning process scenario, future subject profile, simulation of time-series changes in the digital twin environment over a predetermined period, and results selected or proposed by the subject or instructor.

[0079] The timing and frequency of updates to various correspondence tables by the update unit 17 may be based on, for example, the subject's conditioning, or on social data, environmental data, etc., as appropriate. Furthermore, various parameters and relationships may be reflected according to the subject's selection or the instructor's guidance.

[0080] Various correspondence tables may be acquired, for example, by the conditioning provision system 100 and stored in the memory of the conditioning provision system 100 or the conditioning provision device 1, as well as in other terminals 5 or servers 6, etc.

[0081] <Data acquisition unit 11> The data acquisition unit 11 acquires conditioning data of individuals to whom conditioning will be provided in the future. The individuals from whom conditioning data is acquired may be specified, for example, by the individual or by the instructor. The individuals may be specified by referring to database 6 based on items, conditions, etc., such as basic data or attribute data related to the individual. The data acquisition unit 11 acquires the conditioning data of individuals, for example, via individual terminals 2 (2a, 2b), camera 3, and database 6 (6a, 6b), and may also receive it periodically or as needed from individual terminals 2 (2a, 2b), etc. If the data acquisition unit 11 determines that the conditioning data of individuals acquired by the data acquisition unit 11 contains personal information that identifies the individual, for example, the conditioning provision system 100 or the application of individual terminals 2, etc., may delete it in advance or convert it into other attribute data, etc.

[0082] The conditioning data acquired by the data acquisition unit 11 may include, for example, biological data and exercise data related to the subject, as well as at least one of the following: sleep data, dietary data, stress data, activity data related to the subject's activities, or behavioral data related to past behavioral changes provided. The data acquisition unit 11 may acquire conditioning data for multiple subjects periodically, or it may acquire it as needed by referring to, for example, the content of advice and guidance given to the subject by the subject themselves or their instructor, or the history of such interactions. The conditioning data acquired by the data acquisition unit 11 is stored in the storage unit 104 and in a predetermined correspondence table in the database 6.

[0083] Furthermore, for subsequent conditioning data acquisitions (from the nth time onward), the data acquisition unit 11 acquires behavioral data related to behavioral changes that show the results of the subject's actions after the proposal, based on, for example, the future conditioning process scenario generated by the process scenario generation unit 12. The data acquisition unit 11 stores the newly acquired conditioning data and various information used for generation (e.g., conditioning information, future conditioning process scenario, avatar image, etc.) in, for example, the "conditioning data / information table," updating the acquisition date and time information.

[0084] The conditioning data acquired by the data acquisition unit 11 may be acquired, for example, via the communication network 4, through a known NFT (Non-Fungible Token) system, or via a blockchain (not shown), or recorded as related data.

[0085] <Process Scenario Generation Unit 12> The process scenario generation unit 12 generates multiple future conditioning process scenarios corresponding to different behavioral changes of the subject, by referring to the storage unit 104 or the database 6 based on the subject's initial or subsequent conditioning data acquired by the data acquisition unit 11. The process scenario generation unit 12 uses a time-series prediction model trained with data including, for example, the subject's ID or nickname acquired by the data acquisition unit 11, and at least one of other conditioning data (e.g., age, gender, symptoms, activity data, etc.), to refer to the reference database described later and evaluate the subject's current conditioning status (e.g., "tendency towards obesity").

[0086] Furthermore, the process scenario generation unit 12, for example, refers to a reference database and predicts multiple patterns of behavioral change for the subject based on the subject's current conditioning data and evaluation results (tendency towards obesity). It then generates multiple future conditioning process scenarios corresponding to the predicted patterns, such as "Scenario A1 (Able to walk)" which is a process of behavioral change aimed at improving conditioning, "Scenario B1" which is a process for when the behavioral change is the same as the current conditioning, or "Scenario C1 (Risk of fracture)" which is a process for when conditioning deteriorates or when there is a risk of injury.

[0087] As shown in Figure 2, the process scenario generation unit 12, for example, if the person to whom conditioning is to be provided has the ID "12345" and the nickname "Mr. / Ms. ○○", refers to the correspondence table and retrieves the person's attribute data already stored in the database (for example, "Age: 40s", "Gender: Male", "Symptoms: Mild Obesity", "BMI: ***", etc.).

[0088] The process scenario generation unit 12 generates multiple future conditioning process scenarios for a subject based on data about the subject, such as age, gender, and symptoms related to the same person as attribute data, and based on items that can improve the subject's future conditioning (e.g., body shape, weight, symptoms, etc.) and items that remain constant (e.g., gender, age, height, genetic factors, etc.). Based on the acquired conditioning data, the process scenario generation unit 12 identifies items about the subject that may change in the future and items that will not change, and generates multiple conditioning patterned scenarios (process scenarios) for each process according to the subject's behavioral changes.

[0089] The future conditioning process scenarios are generated based on the subject's conditioning data, for example, by creating multiple future conditioning process scenarios such as "Scenario A1 (Walking Possible)," which is a process of behavioral change aimed at improving conditioning; "Scenario B1," which is a process for behavioral change that does not change from the current conditioning; or "Scenario C1 (Risk of Fracture)," which is a process when conditioning deteriorates or there is a risk of injury. The process scenario generation unit 12 predicts the duration of the behavior in the generated multiple future conditioning process scenarios, identifies representative or important conditioning processes in each predicted duration, determines the identified time as a singularity (a point of change in behavioral change), and sets the determined singularity as a branching point in the state transition in the future conditioning process scenario.

[0090] The process scenario generation unit 12 may generate multiple future conditioning process scenarios that predict different behavioral changes by using multiple time-series prediction models trained with data that includes, for example, past conditioning data of the subject, anonymized case data collected from multiple users, social data including economic indices, or environmental data related to climate.

[0091] The process scenario generation unit 12 may calculate evaluation information indicating the degree (difficulty level) of execution or achievement of multiple future conditioning process scenarios, such as the intensity of the scenario process (e.g., activity intensity, diet / nutrition, sleep / rest). The intensity of the process scenario may be calculated, for example, based on the subject's current conditioning data, the difficulty level of execution of each process scenario's menu and tasks, the degree of achievement of behavioral change, difference information (e.g., ●●%~●●%), or it may be calculated or estimated based on various conditions (e.g., A~C). The calculated or estimated intensity of the process scenario may be referenced, for example, when generating an action plan to be presented to the subject.

[0092] <Digital Twin Processing Unit 13> The digital twin calculation unit 13 generates, for example, a current subject profile and a future subject profile that models behavioral changes in a future conditioning process scenario generated by the process scenario generation unit 12, based on conditioning data acquired by the data acquisition unit 11, and simulates time-series changes over a predetermined period in the digital twin environment.

[0093] The digital twin calculation unit 13 refers to the database 6 and generates a current image of the subject, for example, based on the subject's current conditioning data. The current image of the subject may be generated by referring to, for example, realistic images of the subject provided by the subject (e.g., face image, full body image, etc.) as well as the subject's attribute data (e.g., "age: 40s", "gender: male", "symptoms: mild obesity", etc.) to generate images to be used as avatars, such as images or characters. The avatar image generation in the digital twin calculation unit 13 may be performed by, for example, using a known image generation tool, inputting the subject's image and attribute data related to the subject as parameters, and generating multiple avatar images.

[0094] Next, the digital twin calculation unit 13 generates a future subject profile based on the future conditioning process scenario generated by the process scenario generation unit 12. For example, if the future subject profile is "Scenario A1 (Able to walk)" which indicates improved conditioning, it generates an avatar image of the subject with a healthy image. If the future subject profile is "Scenario B1" which indicates no change in current conditioning, it generates an avatar image that is similar to the current subject profile. Furthermore, if the future subject profile is "Scenario C1 (Risk of fracture)" which indicates a decline in conditioning, it may generate an avatar image of the subject with an unhealthy image (fracture, etc.).

[0095] The digital twin calculation unit 13 may generate avatar images of multiple future subjects, for example, according to the simulation results, using the future subject images as the main time axis (e.g., short: a few days, medium: a few months, long: a few years, etc.), and simulate the time-series changes over a predetermined period in the digital twin environment.

[0096] Furthermore, the digital twin calculation unit 13 may generate avatar images that reflect body type, activity level, or health risks as future subject images corresponding to each created future conditioning process scenario, and simulate these avatar images as time-series changes in the digital twin environment. The digital twin calculation unit 13 may, for example, first generate an avatar image of the current subject (current subject image), and then appropriately generate avatar images of the future subject (future subject images) in multiple time series, using information indicating the subject's conditioning status and detailed information of the selected future conditioning process scenario as parameters. The digital twin calculation unit 13 may store the generated current subject image and multiple future subject images in the storage unit 104 or database 6 in association with the future conditioning process scenarios.

[0097] <Display Control Unit 14> The display control unit 14 visualizes the simulation results from the digital twin calculation unit 13 as conditioning information for the subject. The display control unit 14 displays, for example, the current subject's avatar image and avatar images of the subject in multiple future conditioning process scenarios in the digital twin environment. The display control unit 14 may also change and display the avatar images of the subject in the future, which are generated based on the future conditioning process scenarios, based on information about the time series.

[0098] The display control unit 14 displays, for example, the current subject image generated by the digital twin calculation unit 13 and the future subject image of the digital twin corresponding to the future conditioning process scenario in parallel on the display unit 109. In addition, it controls the display of switching between multiple future subject avatar images in response to operations from the subject or instructor. The display control unit 14 acquires operation commands from input units 108 such as the subject device 2 or the conditioning provision device 1, and visualizes the time-series changes of the current subject and future subject avatar images as a simulation in the digital twin environment.

[0099] Furthermore, the display control unit 14 may identify branching points in the state transitions within each future conditioning process scenario set by the process scenario generation unit 12, and present options for behavioral changes related to those branching points. The branching points in the state transitions within each future conditioning process scenario may be, for example, periods of chronological change in conditioning, and the display control may be controlled by switching the future conditioning process scenario at that point in time.

[0100] Here, with reference to Figures 9(a) and 9(b), an example of the simulation results in this embodiment will be explained.

[0101] The simulation results shown in Figure 9(a) display the current profile and avatar image (left side of display unit 109) of the subject (nickname: Ms. XX, age: 40s, gender: female, symptoms: mild obesity, BMI: ***, etc.) as conditioning data, and the avatar image of the future subject in future conditioning process scenario A as conditioning information, displayed on display unit 109 in a digital twin environment.

[0102] In the simulation results shown in Figure 9(a), the current subject profile is the subject's profile data in "Month 2025," and the future subject profile is conditioning information that promotes conditioning as future behavioral changes in "Month 20XX." The conditioning information is a scenario of the process to achieve future conditioning, and the changes in the future subject's conditioning can be visualized over time using, for example, the bar at the bottom of the screen of the display unit 109. The current subject profile shown in Figure 9(a) (left side of the screen of the display unit 109) has a "Details" button, and by pressing this "Details" button, for example, various conditioning data of the current subject can be viewed.

[0103] Furthermore, in the future conditioning process scenario (right side of the screen), various graphs such as a "behavior change graph" and an "action plan" button may be displayed together, for example, as conditioning information related to the future subject profile and the future conditioning process scenario. The "behavior change graph" is, for example, an image that analyzes and aggregates combinations of the main parameters of the future conditioning process scenario in the future subject profile as a multidimensional graph. The "action plan" is conditioning information that the subject or instructor can refer to, for example, a link to detailed information for the subject to implement the future conditioning process scenario. By clicking this "action plan" button, the subject or instructor can provide specific action plan information for the subject.

[0104] Furthermore, the simulation results shown in Figure 9(b) are displayed on the display unit 109 as conditioning information in a digital twin environment, using an avatar image of the subject's future condition in the future conditioning process scenario C shown in Figure 9(a).

[0105] The simulation results shown in Figure 9(b) represent, for example, "Scenario C1 (risk of fracture)," in which conditioning deteriorates, and the future conditioning process scenario (behavioral change) generates an avatar image representing an unhealthy (fracture, etc.) image. In Figure 9(b), the results may also be displayed as graphs (e.g., "Trend 1", "Trend 2") showing the characteristics and trends of behavioral change in the future subject in the future conditioning process scenario.

[0106] Furthermore, the display control unit 14 may generate data / information including at least one of the following as image information: conditioning data / information, scenario data relating to future conditioning process scenarios, behavioral change data relating to behavioral change, avatar data relating to avatars, or behavioral plans, and output the corresponding image information on the display unit 109. The subject or instructor may, for example, read the various image information displayed on the display unit 109 with their own terminal (not shown) to obtain more detailed conditioning information, various related information (e.g., supplements, advice, etc.), token information, etc.

[0107] <Difference analysis section 15> The difference analysis unit 15 evaluates, for example, the difference between the information on the subject's behavioral results acquired by the data acquisition unit 11 and the future behavioral changes in the future conditioning process scenario generated by the process scenario generation unit 12 (the latest current subject profile and the future subject profile in the proposed future conditioning process scenario). The evaluation of the difference in behavioral changes may be done by comparing the components such as parameters and conditions, including "process scenario configuration information," "avatar generation information," and "attribute data," which were referenced when generating the current subject profile and the future subject profile, and then analyzing the difference between the two.

[0108] <Action plan output unit 16> The action plan output unit 16 outputs an action plan regarding a future conditioning process scenario recommended to the current subject or the subject's instructor, based on the difference information evaluated by, for example, the difference analysis unit 15. Now, referring to Figure 10, an example of an action plan provided by the conditioning provision system in this embodiment will be described.

[0109] The action plan shown in Figure 10 can be displayed, for example, by pressing the "Action Plan" button on the display screen of the future conditioning process scenario shown in Figures 9(a) and (b). The action plan output unit 16 generates the action plan based on the subject's conditioning data acquired by the data acquisition unit 11, the future conditioning process scenario generated by the process scenario generation unit 12, the simulation results of the future subject profile generated by the digital twin calculation unit 13, and conditioning information, etc., by referring to the various correspondence tables mentioned above.

[0110] The action plan generated by the action plan output unit 16 is, for example, "Action Plan for Mr. / Ms. XX - Plan No. XXXXX," and outputs the following information regarding the future conditioning process scenario A selected by the subject or instructor: "Action Goals" indicating the goals of the selected process's actions, "Action Period (e.g., short, medium, long, etc.)" indicating the duration of the process, "Recommended Actions" for future conditioning and the future conditioning process scenario, "Activity Intensity" indicating the frequency and duration of the process, "Diet / Nutrition" to be consumed during the process, "Sleep / Rest" during the process, "Behavioral Change Image (Future Subject Profile)," and various graph images. For "Activity Intensity," "Diet / Nutrition," and "Sleep / Rest," more detailed reference information may also be displayed.

[0111] The action plan output unit 16 selects an action plan based on, for example, the "action plan information table" and the current subject's "conditioning data," "current subject profile (avatar image)," the future subject's "future conditioning process scenario," "future subject profile (avatar image)," related image data and differences (differences in each element, each component, etc.), recommended conditions, etc. The action plan output unit 16 may also select an appropriate period (e.g., short, medium, long) from the "action period information" depending on, for example, the future conditioning process scenario and simulation results.

[0112] The action plan output unit 16 may output image data related to, for example, a "behavior change image (future target person profile)" generated by the display control unit 14, a "behavior change graph" displayed as a multidimensional graph, and multiple "behavior goal graphs" showing the behavioral goals of the future target person.

[0113] <Updated part 17> The update unit 17 updates the contents recorded in various information correspondence tables, for example. The update unit 17 updates as appropriate, for example, the generated future conditioning process scenario, the future subject profile, the simulation of time-series changes in the digital twin environment over a predetermined period, and the results selected or proposed by the subject or instructor. The timing and number of times the update unit 17 updates the various correspondence tables are arbitrary. In addition to updating based on the subject's conditioning, for example, the update unit 17 may also update as appropriate in accordance with the timing of updates to social data, environmental data, etc.

[0114] The update unit 17 updates various correspondence tables, for example, based on various data / information acquired by the conditioning provision system 100. The correspondence tables may be stored, for example, in the memory of the conditioning provision system 100, the conditioning provision device 1, or, for example, in another terminal 5 or server 6.

[0115] Furthermore, if the update unit 17 acquires a new relationship between, for example, conditioning data, past conditioning data, and reference information, it reflects that relationship based on the correlation. The update unit 17 may also update the correlation stored in the reference database based on the correlation when the conditioning provision device 1 acquires a judgment result in which an administrator or the like has determined the accuracy of the evaluation results, based on various evaluation results.

[0116] <Communications Department 18> The communication unit 18 receives conditioning data from multiple subjects transmitted from, for example, client terminals (such as the conditioning device 1, subject terminal 2, smartphone 2a, smartwatch 2b, camera 3, and other terminals 5) to the conditioning provision server.

[0117] <Transmitter 19> The transmission unit 19 transmits the results of the simulation performed by the digital twin calculation unit 13, for example, from the conditioning provision server to client terminals (e.g., conditioning provision device 1, subject terminal 2, smartphone 2a, smartwatch 2b, and other terminals 5, etc.).

[0118] <Communication Network 4> The communication network 4 is, for example, the Internet network to which the conditioning provision system 100 (conditioning provision device 1), various target terminals 2 (smartphone 2a, smartwatch 2b) equipped by the target person, camera 3, terminal 5, server 6, etc. are connected via communication circuits. The communication network 4 may be composed of an optical fiber communication network. In addition, the communication network 4 may be implemented using a known communication network other than a wired communication network, such as a wireless communication network. Furthermore, the communication network 4 may be connected to, for example, a known NFT system or blockchain, etc., to enable the transmission, reception, or distribution of various data while preventing tampering or fraud of various conditioning data, future conditioning process scenarios, future target person profiles, and action plans transmitted and received by the conditioning provision system 100.

[0119] <Other devices 5> Other terminals 5 may include, for example, electronic devices similar to the conditioning device 1. Other terminals 5 may include, for example, a central control unit capable of communicating with multiple conditioning devices 1.

[0120] Other terminals 5 can connect to, for example, multiple conditioning provision systems 100 or conditioning provision devices 1. They can refer to conditioning data of multiple subjects acquired by the conditioning provision system 100 or each conditioning provision device 1, future conditioning process scenarios generated as conditioning information, selected future conditioning process scenarios, future subject profiles, and information or images such as action plans. This makes it possible to provide, for example, subject conditioning generated at multiple locations to other terminals 5, and to provide conditioning that promotes future behavioral change according to the generation, background, and purpose of each diverse subject.

[0121] <Server 6 (6a, 6b)> Server 6 stores, for example, the various types of information mentioned above. In addition to storing various types of data such as conditioning data and conditioning information sent via the communication network 4, Server 6 may also store, for example, biometric data, exercise data, sleep data, dietary data, stress data, activity data related to the subject's activities, or behavioral data related to past behavioral changes provided.

[0122] Server 6 may store, for example, past conditioning data of the subject, anonymized case data collected from multiple users, social data including economic indices, or environmental data related to climate, as well as time series prediction models trained using such data. Server 6 may store data / information similar to that of the storage unit 104, and may transmit and receive various information, image data, evaluation results, etc., related to various cameras 3, sensors, etc., between it and one or more conditioning provision systems 100 (conditioning provision devices 1) via the communication network 4. In other words, the conditioning provision device 1 may use Server 6 instead of Storage Unit 104.

[0123] <Reference Database> The reference database stored in the storage unit 104 stores the relationships between previously acquired past conditioning data and reference information (conditioning information, etc.) associated with that past conditioning data, and for example, a learning model with such relationships is stored. The reference database may store, for example, past conditioning data and reference information. The relationships are constructed, for example, by machine learning using multiple training data sets, with past conditioning data and reference information as a set of training data. As a learning method, deep learning such as a convolutional neural network may be used.

[0124] In this case, for example, correlation indicates the degree of connection between many-to-many information (multiple data points included in past conditioning data pairs, and multiple data points included in reference information). Correlation is updated as needed during the machine learning process. That is, correlation represents a function optimized based on, for example, past conditioning data and reference information. Therefore, evaluation results for conditioning data are generated using correlation constructed based on all the results of past evaluations of the subject's conditioning. This makes it possible to generate optimal evaluation results even when the subject's conditioning has complex and diverse states. Furthermore, optimal evaluation results can be quantitatively generated not only when the conditioning data is identical or similar to past conditioning data, but also when it is dissimilar. In addition, by improving the generalization ability when performing machine learning, it is possible to improve the evaluation accuracy for unknown conditioning data.

[0125] Furthermore, the correlation may have multiple correlation degrees, which indicate the degree of connection between multiple data points included in past conditioning data and multiple data points included in reference information. The correlation degree can be associated with weight variables, for example, when the learning model is constructed using a neural network.

[0126] Past conditioning data contains the same type of information as the conditioning data described above. Past conditioning data includes, for example, multiple sets of conditioning data obtained when the conditioning of a subject was evaluated in the past.

[0127] Reference information is linked to past conditioning data and represents data or information regarding the subject's conditioning status. In addition to showing an evaluation based on the subject's conditioning status (e.g., "normal," "abnormal," "within standard," "outside standard"), the reference information may also include various data or information related to the factors influencing the subject's conditioning status. The specific content included in the reference information can be arbitrarily set.

[0128] The target data or information refers to specific state factors such as names, symptoms, and conditions, including attribute data, biometric data, exercise data, health checkup data, activity data, behavioral data, anonymized case data, social data, or environmental data. Each factor generally stems from at least one aspect of the subject's conditioning state.

[0129] The correlation may indicate the degree of connection between past conditioning data and reference information, as shown in Figure 7, for example. In this case, by using correlation, it is possible to associate and store data / information indicating the degree of relationship between each of the multiple data points (in Figure 7, "Reference A" to "Reference C") contained in the past conditioning data. Therefore, for example, by using correlation, it is possible to associate multiple data points contained in the reference information with a single data point contained in the past conditioning data, thereby enabling the generation of multifaceted conditioning evaluation results.

[0130] The correlation has multiple degrees of correlation, linking, for example, multiple data points (target data, target information) included in past conditioning data with multiple data points included in reference information. The degree of correlation is indicated by three or more levels, such as a percentage, a 10-point scale, or a 5-point scale, and is represented by features such as line thickness. For example, "Data A" included in past conditioning data shows a correlation degree AA "80%" with "Reference A" included in reference information, and a correlation degree AB "65%" with "Reference B" included in reference information. In other words, the "degree of correlation" indicates the degree of connection between each data point; for example, a higher degree of correlation indicates a stronger connection between the data points. When constructing the correlation using the machine learning described above, it is also possible to set the correlation to have three or more degree of correlation.

[0131] Past conditioning data may be stored in a reference database by splitting it into past target data A and past target data B, as shown in Figure 8, for example. In this case, the degree of correlation is calculated based on the relationship between the combination of past target data A and past target data B and the reference information. In addition, past conditioning data may also be stored in a reference database by splitting it into past target data C, for example, in addition to the above.

[0132] For example, the combination of "Target Data A1" contained in past target data A and "Target Data B1" contained in past target data B shows a correlation degree of AAA (56%) with "Reference A" and a correlation degree of ABA (23%) with "Reference B". In this case, past target data and past conditioning data can be stored as independent data. Therefore, when generating evaluation results, it becomes possible to improve accuracy and expand the range of options. The above explanation was given for target data, but the same applies to, for example, target information.

[0133] (An example of the operation of the conditioning system 100) Next, an example of the operation of the conditioning provision system 100 in this embodiment will be described. Figure 11 is a flowchart illustrating an example of the operation of the conditioning provision system 100 in this embodiment.

[0134] <Data acquisition step S110> The data acquisition step S110 acquires conditioning data of the subject. The conditioning data acquired in the data acquisition step S110 includes, for example, at least one of the following: biometric data, exercise data, sleep data, dietary data, stress data, activity data related to the subject's activities, or behavioral data related to past behavioral changes provided. The data acquisition step S110 stores the acquired conditioning data in, for example, the storage unit 104 or the database 6.

[0135] The data acquisition step S110 also acquires conditioning data for the subject, for example, the subject themselves or the subject designated by the instructor. The data acquisition step S110 determines, for example, whether the instruction to acquire data from the subject or instructor is the first instruction or a subsequent instruction (e.g., the second instruction or later). As a result, if it is the first instruction to acquire data, all conditioning data is acquired. If it is a subsequent instruction, the system may acquire the difference between the subject's conditioning data and the future conditioning information provided previously, as well as behavioral data related to the subject's behavioral changes performed based on the future conditioning process scenario.

[0136] The data acquisition step S110 may, for example, periodically acquire conditioning data for multiple subjects, or it may also acquire conditioning information by referring to, for example, the content of advice, guidance, etc. given to the subject by the subject themselves or their instructor, or the response history. The conditioning data / conditioning information acquired by the data acquisition step S110 is stored in the storage unit 104 and in a predetermined correspondence table in the database 6.

[0137] <Process Scenario Generation Step S120> Next, the process scenario generation step S120 generates multiple future conditioning process scenarios corresponding to different behavioral changes, based on the conditioning data acquired in the data acquisition step S110.

[0138] The process scenario generation step S120 generates future conditioning process scenarios that predict multiple different behavioral changes, using a time-series prediction model trained with data that includes, for example, past conditioning data of the subject, anonymized case data collected from multiple users, social data including economic indices, or environmental data related to climate.

[0139] The process scenario generation step S120 generates the initial future conditioning process scenario based on the initial conditioning data acquired in the data acquisition step S110, as shown in Figure 2 above. The process scenario is generated based on the subject's attribute data (e.g., "Age: 40s", "Gender: Male", "Symptoms: Mild Obesity", "BMI: ***", etc.) and data on individuals (e.g., anonymized case data) that are similar in gender, age, symptoms, etc. The process scenario generation step S120 generates multiple process scenario patterns based on items that can improve the subject's future conditioning (e.g., body shape, weight, symptoms, etc.) and items that remain constant (e.g., gender, age, height, genetic factors, etc.), for example, focusing on items that can be improved.

[0140] Furthermore, the process scenario generation step S120 refers to the "process scenario information" in the "future conditioning process scenario information table," for example, and generates multiple future conditioning process scenarios tailored to the subject based on each component that constitutes the process scenario (e.g., component A1, component A2, etc.). The differences between each component may be, for example, processes distinguished by items that may change in the future and items that will not change, or scenarios of processes that predict future behavioral changes in the subject (e.g., processes distinguished by "improve conditioning," "maintain current conditioning," and "conditioning deteriorates (warning of unhealthy condition)").

[0141] The process scenario generation step S120 generates future conditioning process scenarios for the nth time onward (from the second time onward). As shown in Figure 3 above, the process scenario generation step S120 generates (updates) future conditioning process scenarios based on the future conditioning process scenario (e.g., process scenario A) specified in the previous proposal of future conditioning information, by referring to the subject's conditioning data newly acquired in the data acquisition step S110 (e.g., behavioral data showing the results of real behavioral changes).

[0142] In the process scenario generation step S120, in the generation of the nth future conditioning process scenario, in addition to continuing the generation of the previously proposed process scenario A, the process scenario generation step S120 may also generate other patterns of future conditioning process scenarios (e.g., process scenario B, process scenario C, etc.).

[0143] Furthermore, the process scenario generation step S120 calculates evaluation information indicating the degree (difficulty level) of execution or achievement of multiple future conditioning process scenarios, such as the intensity of the scenario process (e.g., activity intensity, diet / nutrition, sleep / rest, etc.). The process scenario generation step S120 calculates or estimates the intensity of the process scenario based on, for example, the subject's current conditioning data, the difficulty level of execution of each process scenario's menu and tasks, the degree of achievement of behavioral change, difference information (e.g., ●●%~●●%), or conditions (e.g., A~C, etc.). The calculated or estimated intensity of the process scenario is referenced, for example, when generating an action plan to be presented to the subject.

[0144] <Digital twin calculation step S130> Next, the digital twin calculation step S130 generates an avatar image of the current subject based on, for example, conditioning data. Furthermore, based on the future conditioning process scenario generated in the process scenario generation step S120, it generates a future subject image and conditioning information that model the behavioral changes of the subject. The digital twin calculation step S130 simulates the time-series changes of each avatar image of the generated current subject image and future subject image over a predetermined period in the digital twin environment. The simulation of time-series changes targets, for example, the future subject image in the future (20XX), and generates multiple patterns of avatar images of the future subject image that have been changed according to the future conditioning process scenario.

[0145] The digital twin calculation step S130 generates a current image of the subject based on the subject's current (or latest, after the nth time) conditioning data. The digital twin calculation step S130 may also refer to, for example, real images of the subject provided by the subject (e.g., face image, full body image, etc.) as well as the subject's attribute data (e.g., "age: 40s", "gender: male", "symptoms: mild obesity", etc.) to generate a current image of the subject as an avatar image such as a corresponding image or character. The digital twin calculation step S130 may also use, for example, a known image generation tool to input the subject image and attribute data related to the subject as parameters and conditions to generate multiple avatar images.

[0146] Next, the digital twin calculation step S130 generates an avatar image of the future subject based on the future conditioning process scenario (e.g., process scenario A) generated in the process scenario generation step S120. Multiple patterns of avatar images may be generated for the future subject. For example, in the case of "Scenario A (showing improvement in future conditioning)," an avatar image of a healthy image that is "able to walk" may be generated; in the case of "Scenario B (no change from current conditioning)," an avatar image of an image similar to the current subject may be generated; and in the case of "Scenario C1 (deterioration of conditioning)," an avatar image of an "unhealthy (fracture, etc.)" image may be generated.

[0147] The digital twin calculation step S130 generates, for example, a future subject profile in each future conditioning process scenario as an avatar that includes at least body type, activity level, or health risk, and simulates the time-series changes of the avatar. The digital twin calculation step S130 generates multiple patterns of avatar images for multiple future subject profiles, for example, according to the simulation results. The digital twin calculation step S130 may also generate the processes of the future subject profile as major time axes (e.g., short: a few days, medium: a few months, long: a few years, etc.) based on the target period of the future conditioning process scenario. The digital twin calculation step S130 generates multiple patterns of avatar images of the future subject profile in a time-series change over a predetermined period, which can be simulated in the digital twin environment.

[0148] <Display control step S140> Next, the display control step S140 visualizes the results of the simulation in the digital twin calculation step S130 as conditioning information for the subject. The display control step S140 also visualizes the behavioral changes in the future subject as conditioning information by displaying the current subject image generated in the digital twin calculation step S130 in parallel with the future subject image of the digital twin corresponding to the future conditioning process scenario, or by switching between multiple avatars.

[0149] Furthermore, the display control step S140 identifies branching points in state transitions within each future conditioning process scenario and presents options for behavioral changes related to those branching points.

[0150] In the display control step S140, for example, when displaying the initial future conditioning, the current subject profile (e.g., the subject's conditioning data / information) and the avatar image of the current subject, along with the set future conditioning process scenario and the avatar image of the future subject, are displayed together in a digital twin environment on the display unit 109. In the display control step S140, the avatar image of the future subject is displayed in a time-series progression ("Past (2022)" to "Future (20XX)") in response to the operation of the scroll bar displayed at the bottom of the screen of the display unit 109 by the subject or instructor.

[0151] In the display control step S140, the first time, the avatar image of the current subject and the avatar image of the future subject are displayed in the digital twin environment, and from the nth time onward, the latest avatar image of the current subject and the avatar image of the future subject are displayed in the digital twin environment. In addition, if a time period from "past (2022)" to "future (20XX)" is set in response to the operation of the scroll bar, for example, the display control step S140 may also be configured to display the avatar image of the future subject generated according to the set time period and the latest avatar image of the future subject in the digital twin environment.

[0152] The display control step S140 generates multiple patterns of corresponding image information based on data that includes at least one of the following: conditioning data, scenario data relating to future conditioning process scenarios, behavioral change data relating to behavioral change, avatar data relating to avatars, or behavioral plan data relating to behavioral plans, and outputs the generated image information. The conversion to various image information may be done, for example, by using a known image conversion process to generate and output images such as multiple graphs, multidimensional analysis graphs, various shapes, or codes.

[0153] <Differential Analysis Step S150> Next, the difference analysis step S150 analyzes the difference between the behavioral data acquired in the data acquisition step S110 and the future behavioral changes in the future conditioning process scenario generated in the process scenario generation step S120.

[0154] The difference analysis step S150 calculates the difference between the newly acquired subject's conditioning data and the subject's behavioral changes from the previously generated future conditioning when providing future conditioning for the nth time or later. The process scenario generation step S120 refers to information such as "difference information" and "recommended conditions" in the "behavior plan information table" stored in database 6, for example, and generates a future conditioning process scenario based on the difference calculated in the difference analysis step S150.

[0155] The process scenario generation step S120, in response to the update of difference information by the difference analysis step S150, generates a process scenario that reduces the difference information if it is determined that the difference is large (for example, the hurdles in the process scenario are high), based on, for example, the future conditioning provided previously and the actual behavioral changes of the subject. Alternatively, the process scenario generation step S120 may generate a future conditioning process scenario by referring to, for example, recommended conditions A, B, etc., corresponding to the difference analyzed in the difference analysis step S150. This makes it possible to appropriately evaluate the difference in future behavioral changes (the latest current subject profile and the future subject profile of the proposed future conditioning process scenario), and to provide conditioning that promotes future behavioral changes according to the generation, background, and objectives of diverse subjects.

[0156] <Action plan output step S160> Next, the action plan output step S160 outputs an action plan recommended to the subject based on the difference as conditioning information. The action plan output step S160 outputs an action plan regarding a future conditioning process scenario recommended to the current subject or the subject's instructor, based on the difference information evaluated by the difference analysis step S150, for example. The action plan output step S160 generates an action plan based on the subject's conditioning data acquired by the data acquisition step S110, the future conditioning process scenario generated by the process scenario generation step S120, the simulation results of the future subject profile generated by the digital twin calculation step S130, and conditioning information, for example, by referring to the various correspondence tables mentioned above.

[0157] The action plan output step S160 refers to database 6 and outputs an action plan for each individual, for example, as shown in Figure 10. The action plan is output as, for example, "Action Plan for Mr. / Ms. XX - Plan No. XXXXX" and includes "Action Goals," "Action Period (e.g., short, medium, long, etc.)," ​​"Recommended Actions," "Activity Intensity," "Diet / Nutrition," "Sleep / Rest," "Behavioral Change Image (Future Individual Profile)," and various graph images related to future conditioning process scenario A.

[0158] As shown in Figure 10, the action plan output step S160 may output image data related to the "behavior change image (future target person profile)", "behavior change graph", and "behavior goal graph" generated by the display control step S140.

[0159] This completes the operation of the conditioning provision system 100 in this embodiment. The timing of when the update unit 17 executes the update process is arbitrary.

[0160] Furthermore, according to this embodiment, a conditioning provision method that visualizes future predictions based on the subject's behavior and supports conditioning for each subject can be provided by a data acquisition step S110 for acquiring the subject's conditioning data, a process scenario generation step S120 for generating multiple future conditioning process scenarios corresponding to different behavioral changes based on the conditioning data, a digital twin calculation step S130 for generating a current subject profile and a future subject profile modeling the behavioral changes in the future conditioning process scenarios in a digital twin environment based on the conditioning data, and simulating time-series changes over a predetermined period, and a display control step S140 for visualizing the simulation results as the subject's conditioning information.

[0161] Furthermore, according to this embodiment, the conditioning provision program can be provided by having a computer execute the conditioning provision method.

[0162] Furthermore, the conditioning provision server, which is provided to multiple client terminals via the communication network 4, can be provided by comprising: a communication unit 18 that receives conditioning data of the subject transmitted from the client terminal; a process scenario generation unit 12 that generates multiple future conditioning process scenarios corresponding to different behavioral changes based on the conditioning data received by the communication unit 18; a digital twin calculation unit 13 that generates a current subject profile and a future subject profile that models the behavioral changes in the future conditioning process scenarios based on the conditioning data, and simulates time-series changes over a predetermined period in a digital twin environment; and a transmission unit 19 that transmits the simulation results as conditioning information to the client terminal.

[0163] While embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents. [Explanation of Symbols]

[0164] 1: Conditioning device 2: Target user's device 2a: Smartphone 2b: Smartwatch 3: Camera 4: Communication Network 5: Other devices 6: Server 6a: Server 6b: Server 10: Cabinet 11: Data acquisition unit 12: Process Scenario Generation Unit 13: Digital Twin Processing Unit 14: Display Control Unit 15: Difference analysis section 16: Action plan output unit 17: Update section 18: Communications Department 19: Transmitter 100: Conditioning Provision System 101: CPU 102 :ROM 103: RAM 104: Preservation Department 105 :I / F 106: I / F 107: I / F 108: Input section 109: Display section 110: Internal bus S110: Data acquisition step S120: Process scenario generation step S130: Digital twin calculation step S140: Display control step S150: Difference analysis step S160: Action plan output step

Claims

1. A conditioning provision system that visualizes future predictions based on the behavior of the target individual and supports conditioning for each individual, A data acquisition unit that acquires conditioning data of the subject, A process scenario generation unit inputs the conditioning data acquired by the data acquisition unit into a pre-trained time-series prediction model, and generates multiple future conditioning process scenarios corresponding to different behavioral changes based on the prediction results of multiple different behavioral change patterns output by the time-series prediction model. A digital twin calculation unit generates an avatar representing the current subject based on the conditioning data, generates an avatar representing the future subject that reflects changes in body shape, activity capacity, or health risk corresponding to each behavioral change pattern in the future conditioning process scenario, and simulates the time-series changes from the current subject avatar to the future subject avatar over a predetermined period in a digital twin environment. A display control unit that visualizes the results of the simulation as conditioning information for the subject, A conditioning provision system characterized by comprising the following:

2. The conditioning data acquired by the data acquisition unit includes at least one of the following: biometric data, exercise data, sleep data, dietary data, stress data, activity data relating to the subject's activities, or behavioral data relating to past behavioral changes provided. A conditioning system according to claim 1, characterized by the following:

3. The process scenario generation unit inputs the conditioning data acquired by the data acquisition unit into a time series prediction model trained using data including at least one of the following: past conditioning data of the subject, anonymized case data collected from multiple users, social data including economic indices, or climate-related environmental data. Based on the prediction data of multiple different behavioral changes output from the time series prediction model, it generates future conditioning process scenarios that predict multiple different behavioral changes. A conditioning system according to claim 1, characterized by the following:

4. The aforementioned digital twin computing unit generates a future subject profile for each future conditioning process scenario as an avatar that includes at least body type, activity level, or health risk, and simulates the time-series changes of the avatar. A conditioning system according to claim 1, characterized by the following:

5. The display control unit uses the current subject image generated by the digital twin calculation unit, By displaying the future target person profile of the digital twin corresponding to the aforementioned future conditioning process scenario in parallel, or by switching between multiple avatars, the behavioral changes in the said future target person profile can be visualized. A conditioning system according to claim 4, characterized by the above.

6. The display control unit identifies branching points in state transitions in each future conditioning process scenario and presents options for behavioral changes related to those branching points. A conditioning system according to claim 1, characterized by the following:

7. The data acquisition unit further acquires behavioral data relating to the behavioral changes of the subject carried out based on a future conditioning process scenario. A conditioning system according to claim 1, characterized by the following:

8. A difference analysis unit analyzes the difference between the behavioral data acquired by the data acquisition unit and the future behavioral changes in the generated future conditioning process scenario. The system further comprises an action plan output unit that, based on the difference, refers to the difference information, recommended conditions, or action period information and outputs an action plan recommended for the target person, The action plan output unit generates and outputs an action plan that includes at least one of the following, based on the difference information, the recommended conditions, or the action period information: action goal, action period, recommended action, activity intensity, diet / nutrition, and sleep / rest. A conditioning system according to claim 7, characterized by the following:

9. The display control unit uses the conditioning data acquired by the data acquisition unit, the future conditioning process scenario generated by the process scenario generation unit, the behavioral changes corresponding to the future conditioning process scenario, the avatar representing the subject's image generated by the digital twin calculation unit, or the behavioral plan output unit to generate image information including graphs or figures to explain these contents, separately from the visualization of the simulation results described in claim 1, and outputs the image information. A conditioning system according to claim 8, characterized by the following:

10. A conditioning provision method that visualizes future predictions based on the behavior of a subject, which are performed on a computer, and supports conditioning for each subject, A data acquisition step to obtain conditioning data of the subject, A process scenario generation step involves inputting the conditioning data acquired in the data acquisition step into a pre-trained time-series prediction model, and generating multiple future conditioning process scenarios corresponding to different behavioral changes based on the prediction results of multiple different behavioral change patterns output by the time-series prediction model. A digital twin calculation step that generates an avatar representing the current subject based on the conditioning data, generates an avatar representing the future subject that reflects changes in body shape, activity capacity, or health risk corresponding to each behavioral change pattern in the future conditioning process scenario, and simulates the time-series changes from the current subject avatar to the future subject avatar over a predetermined period in a digital twin environment, A display control step that visualizes the results of the simulation as conditioning information for the subject, A method for providing conditioning, characterized by having [a certain feature].

11. On the computer, To perform the conditioning provision method described in claim 10. A conditioning program characterized by the following features.

12. A conditioning server provided to multiple client terminals via a network, A communication unit that receives the subject's conditioning data transmitted from the client terminal, A process scenario generation unit inputs the conditioning data acquired by the communication unit into a pre-trained time-series prediction model, and generates multiple future conditioning process scenarios corresponding to different behavioral changes based on the prediction results of multiple different behavioral change patterns output by the time-series prediction model. A digital twin calculation unit generates an avatar representing the current subject based on the conditioning data, generates an avatar representing the future subject that reflects changes in body shape, activity capacity, or health risk corresponding to each behavioral change pattern in the future conditioning process scenario, and simulates the time-series changes from the current subject avatar to the future subject avatar over a predetermined period in a digital twin environment. A transmission unit that transmits the results of the simulation to the client terminal, A conditioning server characterized by being equipped with the following features.

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